04-Covid19_Dashboard

COVID 19 Dashboard for Balkan countries*

Here you go directly to the dashboard

Data

# Github 
covid19_confirmed_git <-"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
covid19_confirmed_git <- read_csv(url(covid19_confirmed_git))

# Worldometer
## Covid
codivid19_all <- "https://www.worldometers.info/coronavirus/"
main_table <- codivid19_all%>%
  xml2::read_html()%>%
  html_nodes(xpath='//*[@id="main_table_countries_today"]') %>%
  html_table()
main_table <- as.data.frame(main_table)

## Filtering data for Balkan countries (plus Italy and Austria)
balkan <- filter(main_table,Country.Other == "Bosnia and Herzegovina" | Country.Other == "Italy" | Country.Other == "Croatia" | Country.Other == "Serbia"  | Country.Other == "Montenegro"  | Country.Other == "Slovenia"  | Country.Other == "Austria"  | Country.Other == "North Macedonia" | Country.Other == "Greece")

# Removing comma from numbers
balkan[c("TotalRecovered","TotalDeaths","TotalCases","NewCases","ActiveCases","Tot.Cases.1M.pop")] <- lapply(balkan[c("TotalRecovered","TotalDeaths","TotalCases","NewCases","ActiveCases","Tot.Cases.1M.pop")], function(x) gsub(",","",x))
# Turning columns to numeric
balkan[c("TotalRecovered","TotalDeaths","TotalCases","NewCases","ActiveCases","Tot.Cases.1M.pop")] <- lapply(balkan[c("TotalRecovered","TotalDeaths","TotalCases","NewCases","ActiveCases","Tot.Cases.1M.pop")], as.numeric)

Curve of confirmed cases

The graph shows the number of confirmed cases by the last date shown.Updates daily at around 23:59 UTC.

# Curve of confirmed cases----
columns <- colnames(covid19_confirmed_git)[5:ncol(covid19_confirmed_git)]
final <-as.data.frame(pivot_longer(covid19_confirmed_git, cols = columns, names_to = "Year", values_to = "Confirmed"))
final$Year <- as.Date.character(final$Year,"%m/%d/%y")
colnames(final) <- c("Province","Country","Lat","Long","Year","Confirmed") 
filter <- filter(final, Country == "Bosnia and Herzegovina" | Country == "Italy" | Country == "Croatia" | Country == "Serbia"  | Country == "Slovenia"  | Country == "Montenegro"  | Country == "Austria" | Country == "North Macedonia" | Country == "Greece")
p <-ggplot(filter, aes(x = Year, y = Confirmed)) + 
  geom_line(aes(color = Country), size = 1) +
  scale_color_brewer(palette="Set1")+
  theme(legend.title = element_text(size = 6),legend.text = element_text(size = 6),  
  # Remove panel background
  panel.background = element_blank(),
  # Add axis line
  axis.line = element_line(colour = "grey"))+
  scale_y_log10(labels = comma)+
  scale_x_date(date_labels = "%b-%d", date_breaks = "4 week")+
  ylab("Confirmed cases")+
  labs(caption="Data source: https://github.com/CSSEGISandData/COVID-19")
ggplotly(p)

Total cases per 1 million people

tot_cases_1m <- melt(balkan[,c("Tot.Cases.1M.pop","Country.Other")])
head(tot_cases_1m)

p<-ggplot(tot_cases_1m, aes(x=Country.Other,y=value,fill=Country.Other)) +
  geom_bar(stat = "identity")+
  scale_fill_manual(name="Country",
                    values = c("#E41A1C",
                               "#377EB8",
                               "#4DAF4A",
                               "#984EA3",
                               "#FF7F00",
                               "#FFFF33",
                               "#A65628",
                               "#F781BF",
                               "#999999"),
                    labels=c("Austria",
                             "Bosnia and Herzegovina",
                             "Croatia",
                             "Greece",
                             "Italy",
                             "Montenegro",
                             "N.Macedonia",
                             "Serbia",
                             "Slovenia"))+
  labs(x="",y="Total Cases per 1M people", title = "Total Cases per 1m people - Currently")+
  theme(legend.title = element_text(size = 8),
        axis.text.x = element_blank(),
        legend.text = element_text(size = 8),
        panel.background = element_blank(),
        axis.line = element_line(colour = "grey"))

ggplotly(p)

NA

Deaths vs Recovered

The bar chart shows the total number of recovered people in comparison to the total number of death cases. Updates daily at around 23:59 UTC.

options(scipen = 9999)
p <- ggplot(balkan) +
  geom_segment( aes(x=Country.Other, xend=Country.Other, y=TotalRecovered, yend=TotalDeaths), color="grey") +
  geom_point( aes(x=Country.Other, y=TotalRecovered), color=rgb(0.2,0.7,0.1,0.5), size=3 ) +
  geom_point( aes(x=Country.Other, y=TotalDeaths), color=rgb(0.7,0.2,0.1,0.5), size=3 ) + coord_flip()+
  scale_y_log10()+
  theme_minimal() +
  theme(
  legend.position = "none",
  panel.background = element_blank(),
  panel.grid = element_blank(),
  axis.line = element_line(colour = "grey")) +
    xlab("") +
    ylab("Number of cases")+
    ggtitle(label = "Death vs Recovered")
ggplotly(p)

Active cases vs New cases

The bar chart shows the number of active cases in comparison to the number of new cases being constantly reported.

# Total cases vs new cases
total_and_new <- melt(balkan[,c("TotalCases","NewCases","Country.Other")])
p <- ggplot(total_and_new, aes(x=Country.Other, y=value, fill=variable)) +
  geom_bar(stat='identity', position='dodge', color="black" ,aes(text=paste("Country: ",Country.Other, "\n", variable,":",value, sep=""))) +
  scale_fill_brewer(palette = "Paired")+
  scale_y_continuous(labels=comma, trans = "log10") +
  ylab("Number of cases")+
  xlab("")+
  ggtitle(label = "Total cases vs New cases") +
  theme_minimal()+
  labs(title = "",fill="" )+
  coord_flip()
  
ggplotly(p, tooltip = "text")

Hospitalization

Croatia

Slovenia

Austria